TensorFlow Machine Learning Cookbook

Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook

TensorFlow Machine Learning Cookbook

This ebook is included in a Mapt subscription
Nick McClure

Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook
$0.00
$22.00
$54.99
$29.99p/m after trial
RRP $43.99
RRP $54.99
Subscription
eBook
Print + eBook
Start 30 Day Trial
Subscribe and access every Packt eBook & Video.
 
  • 4,000+ eBooks & Videos
  • 40+ New titles a month
  • 1 Free eBook/Video to keep every month
Start Free Trial
 
Preview in Mapt

Book Details

ISBN 139781786462169
Paperback370 pages

Book Description

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.

This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.

Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.

Table of Contents

Chapter 1: Getting Started with TensorFlow
Introduction
How TensorFlow Works
Declaring Tensors
Using Placeholders and Variables
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
Additional Resources
Chapter 2: The TensorFlow Way
Introduction
Operations in a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
Chapter 3: Linear Regression
Introduction
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning The TensorFlow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression
Chapter 4: Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non-Linear SVM
Implementing a Multi-Class SVM
Chapter 5: Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Working with Text-Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
Chapter 6: Neural Networks
Introduction
Implementing Operational Gates
Working with Gates and Activation Functions
Implementing a One-Layer Neural Network
Implementing Different Layers
Using a Multilayer Neural Network
Improving the Predictions of Linear Models
Learning to Play Tic Tac Toe
Chapter 7: Natural Language Processing
Introduction
Working with bag of words
Implementing TF-IDF
Working with Skip-gram Embeddings
Working with CBOW Embeddings
Making Predictions with Word2vec
Using Doc2vec for Sentiment Analysis
Chapter 8: Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet/Neural-Style
Implementing DeepDream
Chapter 9: Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure
Chapter 10: Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow – An Example
Chapter 11: More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
There's more…
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs

What You Will Learn

  • Become familiar with the basics of the TensorFlow machine learning library
  • Get to know Linear Regression techniques with TensorFlow
  • Learn SVMs with hands-on recipes
  • Implement neural networks and improve predictions
  • Apply NLP and sentiment analysis to your data
  • Master CNN and RNN through practical recipes
  • Take TensorFlow into production

Authors

Table of Contents

Chapter 1: Getting Started with TensorFlow
Introduction
How TensorFlow Works
Declaring Tensors
Using Placeholders and Variables
Working with Matrices
Declaring Operations
Implementing Activation Functions
Working with Data Sources
Additional Resources
Chapter 2: The TensorFlow Way
Introduction
Operations in a Computational Graph
Layering Nested Operations
Working with Multiple Layers
Implementing Loss Functions
Implementing Back Propagation
Working with Batch and Stochastic Training
Combining Everything Together
Evaluating Models
Chapter 3: Linear Regression
Introduction
Using the Matrix Inverse Method
Implementing a Decomposition Method
Learning The TensorFlow Way of Linear Regression
Understanding Loss Functions in Linear Regression
Implementing Deming regression
Implementing Lasso and Ridge Regression
Implementing Elastic Net Regression
Implementing Logistic Regression
Chapter 4: Support Vector Machines
Introduction
Working with a Linear SVM
Reduction to Linear Regression
Working with Kernels in TensorFlow
Implementing a Non-Linear SVM
Implementing a Multi-Class SVM
Chapter 5: Nearest Neighbor Methods
Introduction
Working with Nearest Neighbors
Working with Text-Based Distances
Computing with Mixed Distance Functions
Using an Address Matching Example
Using Nearest Neighbors for Image Recognition
Chapter 6: Neural Networks
Introduction
Implementing Operational Gates
Working with Gates and Activation Functions
Implementing a One-Layer Neural Network
Implementing Different Layers
Using a Multilayer Neural Network
Improving the Predictions of Linear Models
Learning to Play Tic Tac Toe
Chapter 7: Natural Language Processing
Introduction
Working with bag of words
Implementing TF-IDF
Working with Skip-gram Embeddings
Working with CBOW Embeddings
Making Predictions with Word2vec
Using Doc2vec for Sentiment Analysis
Chapter 8: Convolutional Neural Networks
Introduction
Implementing a Simpler CNN
Implementing an Advanced CNN
Retraining Existing CNNs models
Applying Stylenet/Neural-Style
Implementing DeepDream
Chapter 9: Recurrent Neural Networks
Introduction
Implementing RNN for Spam Prediction
Implementing an LSTM Model
Stacking multiple LSTM Layers
Creating Sequence-to-Sequence Models
Training a Siamese Similarity Measure
Chapter 10: Taking TensorFlow to Production
Introduction
Implementing unit tests
Using Multiple Executors
Parallelizing TensorFlow
Taking TensorFlow to Production
Productionalizing TensorFlow – An Example
Chapter 11: More with TensorFlow
Introduction
Visualizing graphs in Tensorboard
There's more…
Working with a Genetic Algorithm
Clustering Using K-Means
Solving a System of ODEs

Book Details

ISBN 139781786462169
Paperback370 pages
Read More

Read More Reviews